import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def 				lowerCAmelCase__       (			*a__ )      ->str:




  '''simple docstring'''

  if not isinstance(a__    ,     a__ ):
    _UpperCamelCase           =    list(a__ )
  for i in range(len(a__ ) ):
    _UpperCamelCase           =    None
  gc.collect()
  if is_xpu_available():
    torch.xpu.empty_cache()
  elif is_npu_available():
    torch.npu.empty_cache()
  else:
    torch.cuda.empty_cache()
  return objects


def 				lowerCAmelCase__       (			a__ )      ->bool:




  '''simple docstring'''

  _UpperCamelCase           =    [
      "CUDA out of memory.",  # CUDA OOM
      "cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.",  # CUDNN SNAFU
      "DefaultCPUAllocator: can't allocate memory",  # CPU OOM
  ]
  if isinstance(a__    ,     a__ ) and len(exception.args ) == 1:
    return any(err in exception.args[0] for err in _statements )
  return False







def 				lowerCAmelCase__       (			a__ = None    ,     a__ = 128 )      ->Dict:




  '''simple docstring'''

  if function is None:
    return functools.partial(a__    ,     starting_batch_size=a__ )

  _UpperCamelCase           =    starting_batch_size

  def decorator(*a__    ,     **a__ ):
    nonlocal batch_size
    gc.collect()
    if is_xpu_available():
      torch.xpu.empty_cache()
    elif is_npu_available():
      torch.npu.empty_cache()
    else:
      torch.cuda.empty_cache()
    _UpperCamelCase           =    list(inspect.signature(a__ ).parameters.keys() )
    # Guard against user error
    if len(a__ ) < (len(a__ ) + 1):
      _UpperCamelCase           =    ", ".join([f'{arg}={value}' for arg, value in zip(params[1:]    ,     args[1:] )] )
      raise TypeError(
          f'Batch size was passed into `{function.__name__}` as the first argument when called.'
          f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`' )
    while True:
      if batch_size == 0:
        raise RuntimeError("No executable batch size found, reached zero." )
      try:
        return function(a__    ,     *a__    ,     **a__ )
      except Exception as e:
        if should_reduce_batch_size(a__ ):
          gc.collect()
          if is_xpu_available():
            torch.xpu.empty_cache()
          elif is_npu_available():
            torch.npu.empty_cache()
          else:
            torch.cuda.empty_cache()
          batch_size //= 2
        else:
          raise

  return decorator





import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )




@require_torch
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Dict)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    3
   _UpperCamelCase           =    250

   _UpperCamelCase           =    ids_tensor((batch_size, length)							, lowercase_)
   _UpperCamelCase           =    torch.ones((batch_size, length)							, device=lowercase_							, dtype=torch.float) / length
   return input_ids, scores







 def 		__UpperCAmelCase				(		self				:   Any)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    StoppingCriteriaList(
       [
           MaxLengthCriteria(max_length=10),
           MaxTimeCriteria(max_time=0.1),
       ])

   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxLengthCriteria(max_length=10)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxNewTokensCriteria(start_length=5							, max_new_tokens=5)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    StoppingCriteriaList([criteria])
   self.assertEqual(criteria_list.max_length							, 10)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1							, initial_timestamp=time.time() - 0.2)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Tuple:


   """simple docstring"""







   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 10)

   with self.assertWarns(lowercase_):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 11)

   _UpperCamelCase           =    validate_stopping_criteria(StoppingCriteriaList()							, 11)

   self.assertEqual(len(lowercase_)							, 1)




